A Bayesian Ensemble Regression Framework on the Angry Birds Game
نویسندگان
چکیده
منابع مشابه
A CBR Approach to the Angry Birds Game
In this paper, we present a CBR approach for implementing an agent playing the well-known Angry Birds game. We adopt a preference-based procedure for constructing the case base, collecting experience from a random agent that continually explores the problemsolution space and improves the quality of already found solutions. As the retrieve phase involves finding a game scene similar to a given o...
متن کاملAngry Birds, Angry Children, and Angry Meta-Analysts: A Reanalysis.
Ferguson's (2015a) meta-analysis assessed a very important and controversial topic about children's mental health and video games. In response to the concerns raised by researchers about the appropriateness of the meta-analytical methods used by Ferguson; we decided to reanalyze the data and discuss two major misconceptions about meta-analysis. We argue that partial correlations can (and should...
متن کاملDiscovering Regression Structure with a Bayesian Ensemble
A Bayesian ensemble can be used to discover and learn about the regression relationship between a variable of interest y, and p potential predictor variables x1, . . . , xp. The basic idea is to model the conditional distribution of y given x by a sum of random basis elements plus a flexible noise distribution. In my Loeb Research Lecture, I will focus on a Bayesian ensemble approach called BAR...
متن کاملThe Angry Birds AI Competition
SUMMER 2015 85 The aim of the Angry Birds AI competition (AIBIRDS) is to build intelligent agents that can play novel Angry Birds levels better than the best human players. The competition was initiated in 2012 by the authors of this report and is held in collocation with some of the major AI conferences such as the International Joint Conference on Artificial Intelligence in 2013 and again in ...
متن کاملA Bayesian Framework for Online Classifier Ensemble
We propose a Bayesian framework for recursively estimating the classifier weights in online learning of a classifier ensemble. In contrast with past methods, such as stochastic gradient descent or online boosting, our framework estimates the weights in terms of evolving posterior distributions. For a specified class of loss functions, we show that it is possible to formulate a suitably defined ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Computational Intelligence and AI in Games
سال: 2016
ISSN: 1943-068X,1943-0698
DOI: 10.1109/tciaig.2015.2494679